Overview

Dataset statistics

Number of variables15
Number of observations985
Missing cells0
Missing cells (%)0.0%
Duplicate rows66
Duplicate rows (%)6.7%
Total size in memory109.7 KiB
Average record size in memory114.0 B

Variable types

Boolean2
Categorical5
Numeric8

Alerts

Transported has constant value ""Constant
Dataset has 66 (6.7%) duplicate rowsDuplicates
Cabin_deck is highly overall correlated with Consumption_Basic and 4 other fieldsHigh correlation
Consumption_Basic is highly overall correlated with Cabin_deck and 6 other fieldsHigh correlation
Consumption_High_End is highly overall correlated with Cabin_deck and 6 other fieldsHigh correlation
CryoSleep is highly overall correlated with Cabin_deck and 2 other fieldsHigh correlation
FoodCourt is highly overall correlated with Cabin_deck and 5 other fieldsHigh correlation
HomePlanet is highly overall correlated with Cabin_deckHigh correlation
RoomService is highly overall correlated with Consumption_Basic and 2 other fieldsHigh correlation
ShoppingMall is highly overall correlated with Consumption_Basic and 3 other fieldsHigh correlation
Spa is highly overall correlated with Consumption_Basic and 3 other fieldsHigh correlation
VRDeck is highly overall correlated with Consumption_Basic and 3 other fieldsHigh correlation
VIP is highly imbalanced (91.2%)Imbalance
RoomService has 832 (84.5%) zerosZeros
FoodCourt has 750 (76.1%) zerosZeros
ShoppingMall has 750 (76.1%) zerosZeros
Spa has 823 (83.6%) zerosZeros
VRDeck has 829 (84.2%) zerosZeros
Consumption_High_End has 710 (72.1%) zerosZeros
Consumption_Basic has 660 (67.0%) zerosZeros

Reproduction

Analysis started2024-05-07 10:35:50.950691
Analysis finished2024-05-07 10:36:05.176530
Duration14.23 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

CryoSleep
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.7 KiB
True
536 
False
449 
ValueCountFrequency (%)
True 536
54.4%
False 449
45.6%
2024-05-07T12:36:05.310811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Destination
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
TRAPPIST-1e
680 
PSO J318.5-22
164 
55 Cancri e
141 

Length

Max length13
Median length11
Mean length11.332995
Min length11

Characters and Unicode

Total characters11163
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTRAPPIST-1e
2nd rowTRAPPIST-1e
3rd row55 Cancri e
4th row55 Cancri e
5th rowPSO J318.5-22

Common Values

ValueCountFrequency (%)
TRAPPIST-1e 680
69.0%
PSO J318.5-22 164
 
16.6%
55 Cancri e 141
 
14.3%

Length

2024-05-07T12:36:05.466111image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T12:36:05.648446image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
trappist-1e 680
47.5%
pso 164
 
11.5%
j318.5-22 164
 
11.5%
55 141
 
9.9%
cancri 141
 
9.9%
e 141
 
9.9%

Most occurring characters

ValueCountFrequency (%)
P 1524
13.7%
T 1360
12.2%
S 844
 
7.6%
- 844
 
7.6%
1 844
 
7.6%
e 821
 
7.4%
A 680
 
6.1%
I 680
 
6.1%
R 680
 
6.1%
446
 
4.0%
Other values (13) 2440
21.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11163
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 1524
13.7%
T 1360
12.2%
S 844
 
7.6%
- 844
 
7.6%
1 844
 
7.6%
e 821
 
7.4%
A 680
 
6.1%
I 680
 
6.1%
R 680
 
6.1%
446
 
4.0%
Other values (13) 2440
21.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11163
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 1524
13.7%
T 1360
12.2%
S 844
 
7.6%
- 844
 
7.6%
1 844
 
7.6%
e 821
 
7.4%
A 680
 
6.1%
I 680
 
6.1%
R 680
 
6.1%
446
 
4.0%
Other values (13) 2440
21.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11163
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 1524
13.7%
T 1360
12.2%
S 844
 
7.6%
- 844
 
7.6%
1 844
 
7.6%
e 821
 
7.4%
A 680
 
6.1%
I 680
 
6.1%
R 680
 
6.1%
446
 
4.0%
Other values (13) 2440
21.9%

VIP
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.7 KiB
False
974 
True
 
11
ValueCountFrequency (%)
False 974
98.9%
True 11
 
1.1%
2024-05-07T12:36:05.792541image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

RoomService
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct90
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.919656
Minimum0
Maximum2050
Zeros832
Zeros (%)84.5%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2024-05-07T12:36:06.025394image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile79.241642
Maximum2050
Range2050
Interquartile range (IQR)0

Descriptive statistics

Standard deviation101.3864
Coefficient of variation (CV)6.3686304
Kurtosis250.51102
Mean15.919656
Median Absolute Deviation (MAD)0
Skewness14.23984
Sum15680.861
Variance10279.203
MonotonicityNot monotonic
2024-05-07T12:36:06.268579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 832
84.5%
4 11
 
1.1%
1 10
 
1.0%
2 8
 
0.8%
3 6
 
0.6%
7 6
 
0.6%
21 4
 
0.4%
9 3
 
0.3%
44 3
 
0.3%
13 3
 
0.3%
Other values (80) 99
 
10.1%
ValueCountFrequency (%)
0 832
84.5%
1 10
 
1.0%
2 8
 
0.8%
3 6
 
0.6%
4 11
 
1.1%
5 3
 
0.3%
6 1
 
0.1%
7 6
 
0.6%
8 2
 
0.2%
9 3
 
0.3%
ValueCountFrequency (%)
2050 1
0.1%
1722 1
0.1%
726 1
0.1%
590 1
0.1%
528 1
0.1%
485 1
0.1%
416 1
0.1%
379 1
0.1%
368 1
0.1%
366 1
0.1%

FoodCourt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct210
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean296.45313
Minimum0
Maximum9532
Zeros750
Zeros (%)76.1%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2024-05-07T12:36:06.476562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1625.6
Maximum9532
Range9532
Interquartile range (IQR)0

Descriptive statistics

Standard deviation949.35008
Coefficient of variation (CV)3.2023615
Kurtosis33.189657
Mean296.45313
Median Absolute Deviation (MAD)0
Skewness5.2297744
Sum292006.33
Variance901265.58
MonotonicityNot monotonic
2024-05-07T12:36:06.739536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 750
76.1%
3 4
 
0.4%
9 3
 
0.3%
10 2
 
0.2%
652 2
 
0.2%
6 2
 
0.2%
746 2
 
0.2%
810 2
 
0.2%
837 2
 
0.2%
702 2
 
0.2%
Other values (200) 214
 
21.7%
ValueCountFrequency (%)
0 750
76.1%
1 2
 
0.2%
2 2
 
0.2%
3 4
 
0.4%
4 1
 
0.1%
6 2
 
0.2%
9 3
 
0.3%
10 2
 
0.2%
12 2
 
0.2%
13 2
 
0.2%
ValueCountFrequency (%)
9532 1
0.1%
8525 1
0.1%
8252 1
0.1%
7201 1
0.1%
6819 1
0.1%
6153 1
0.1%
6073 1
0.1%
6050 1
0.1%
5637 1
0.1%
5407 1
0.1%

ShoppingMall
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct209
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173.18381
Minimum0
Maximum4333
Zeros750
Zeros (%)76.1%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2024-05-07T12:36:07.039227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile993.4
Maximum4333
Range4333
Interquartile range (IQR)0

Descriptive statistics

Standard deviation439.46791
Coefficient of variation (CV)2.5375808
Kurtosis18.660081
Mean173.18381
Median Absolute Deviation (MAD)0
Skewness3.6539135
Sum170586.05
Variance193132.04
MonotonicityNot monotonic
2024-05-07T12:36:07.319399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 750
76.1%
2 5
 
0.5%
1 4
 
0.4%
787 2
 
0.2%
46 2
 
0.2%
718 2
 
0.2%
600 2
 
0.2%
896 2
 
0.2%
959 2
 
0.2%
17 2
 
0.2%
Other values (199) 212
 
21.5%
ValueCountFrequency (%)
0 750
76.1%
1 4
 
0.4%
2 5
 
0.5%
3 2
 
0.2%
4 1
 
0.1%
5 2
 
0.2%
8 1
 
0.1%
9 2
 
0.2%
11 1
 
0.1%
12 1
 
0.1%
ValueCountFrequency (%)
4333 1
0.1%
3700 1
0.1%
2817 1
0.1%
2687 1
0.1%
2566 1
0.1%
2473 1
0.1%
2370 1
0.1%
2196 1
0.1%
2116 1
0.1%
2020 1
0.1%

Spa
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct103
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.841011
Minimum0
Maximum2799
Zeros823
Zeros (%)83.6%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2024-05-07T12:36:07.514970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile128.6
Maximum2799
Range2799
Interquartile range (IQR)0

Descriptive statistics

Standard deviation197.9113
Coefficient of variation (CV)5.521923
Kurtosis84.708123
Mean35.841011
Median Absolute Deviation (MAD)0
Skewness8.488326
Sum35303.395
Variance39168.883
MonotonicityNot monotonic
2024-05-07T12:36:07.718858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 823
83.6%
1 9
 
0.9%
2 9
 
0.9%
3 8
 
0.8%
46 4
 
0.4%
4 4
 
0.4%
15 3
 
0.3%
26 3
 
0.3%
21 3
 
0.3%
16 3
 
0.3%
Other values (93) 116
 
11.8%
ValueCountFrequency (%)
0 823
83.6%
1 9
 
0.9%
2 9
 
0.9%
3 8
 
0.8%
4 4
 
0.4%
5 3
 
0.3%
6 2
 
0.2%
7 3
 
0.3%
7.395469138 1
 
0.1%
8 2
 
0.2%
ValueCountFrequency (%)
2799 1
0.1%
2326 1
0.1%
1936 1
0.1%
1643 1
0.1%
1589 1
0.1%
1482 1
0.1%
1438 1
0.1%
1400 1
0.1%
1368 1
0.1%
1313 1
0.1%

VRDeck
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct103
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.878867
Minimum0
Maximum3027
Zeros829
Zeros (%)84.2%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2024-05-07T12:36:07.952342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile95.379792
Maximum3027
Range3027
Interquartile range (IQR)0

Descriptive statistics

Standard deviation214.27307
Coefficient of variation (CV)5.5112993
Kurtosis81.997533
Mean38.878867
Median Absolute Deviation (MAD)0
Skewness8.2608207
Sum38295.684
Variance45912.95
MonotonicityNot monotonic
2024-05-07T12:36:08.180073image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 829
84.2%
1 10
 
1.0%
3 8
 
0.8%
2 6
 
0.6%
13 5
 
0.5%
4 4
 
0.4%
8 4
 
0.4%
10 3
 
0.3%
7 3
 
0.3%
15 3
 
0.3%
Other values (93) 110
 
11.2%
ValueCountFrequency (%)
0 829
84.2%
1 10
 
1.0%
2 6
 
0.6%
3 8
 
0.8%
4 4
 
0.4%
5 2
 
0.2%
6 1
 
0.1%
7 3
 
0.3%
8 4
 
0.4%
9 2
 
0.2%
ValueCountFrequency (%)
3027 1
0.1%
2587 1
0.1%
1933 1
0.1%
1920 1
0.1%
1666 1
0.1%
1462 1
0.1%
1388 1
0.1%
1272 1
0.1%
1244 1
0.1%
1156 1
0.1%

Cabin_deck
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
G
632 
F
182 
E
81 
B
 
30
A
 
22
Other values (2)
 
38

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters985
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowG
3rd rowE
4th rowE
5th rowE

Common Values

ValueCountFrequency (%)
G 632
64.2%
F 182
 
18.5%
E 81
 
8.2%
B 30
 
3.0%
A 22
 
2.2%
C 21
 
2.1%
D 17
 
1.7%

Length

2024-05-07T12:36:08.397084image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T12:36:08.558449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
g 632
64.2%
f 182
 
18.5%
e 81
 
8.2%
b 30
 
3.0%
a 22
 
2.2%
c 21
 
2.1%
d 17
 
1.7%

Most occurring characters

ValueCountFrequency (%)
G 632
64.2%
F 182
 
18.5%
E 81
 
8.2%
B 30
 
3.0%
A 22
 
2.2%
C 21
 
2.1%
D 17
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 985
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 632
64.2%
F 182
 
18.5%
E 81
 
8.2%
B 30
 
3.0%
A 22
 
2.2%
C 21
 
2.1%
D 17
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 985
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 632
64.2%
F 182
 
18.5%
E 81
 
8.2%
B 30
 
3.0%
A 22
 
2.2%
C 21
 
2.1%
D 17
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 985
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 632
64.2%
F 182
 
18.5%
E 81
 
8.2%
B 30
 
3.0%
A 22
 
2.2%
C 21
 
2.1%
D 17
 
1.7%

Group_size
Real number (ℝ)

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0730964
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2024-05-07T12:36:08.726645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7199029
Coefficient of variation (CV)0.82962997
Kurtosis2.5916701
Mean2.0730964
Median Absolute Deviation (MAD)0
Skewness1.8153092
Sum2042
Variance2.9580661
MonotonicityNot monotonic
2024-05-07T12:36:08.880573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 580
58.9%
2 140
 
14.2%
3 121
 
12.3%
5 42
 
4.3%
4 33
 
3.4%
7 29
 
2.9%
6 23
 
2.3%
8 17
 
1.7%
ValueCountFrequency (%)
1 580
58.9%
2 140
 
14.2%
3 121
 
12.3%
4 33
 
3.4%
5 42
 
4.3%
6 23
 
2.3%
7 29
 
2.9%
8 17
 
1.7%
ValueCountFrequency (%)
8 17
 
1.7%
7 29
 
2.9%
6 23
 
2.3%
5 42
 
4.3%
4 33
 
3.4%
3 121
 
12.3%
2 140
 
14.2%
1 580
58.9%

HomePlanet
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
Earth
812 
Mars
92 
Europa
 
81

Length

Max length6
Median length5
Mean length4.9888325
Min length4

Characters and Unicode

Total characters4914
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEuropa
2nd rowEarth
3rd rowEarth
4th rowEarth
5th rowEarth

Common Values

ValueCountFrequency (%)
Earth 812
82.4%
Mars 92
 
9.3%
Europa 81
 
8.2%

Length

2024-05-07T12:36:09.104551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T12:36:09.255661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
earth 812
82.4%
mars 92
 
9.3%
europa 81
 
8.2%

Most occurring characters

ValueCountFrequency (%)
a 985
20.0%
r 985
20.0%
E 893
18.2%
t 812
16.5%
h 812
16.5%
M 92
 
1.9%
s 92
 
1.9%
u 81
 
1.6%
o 81
 
1.6%
p 81
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4914
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 985
20.0%
r 985
20.0%
E 893
18.2%
t 812
16.5%
h 812
16.5%
M 92
 
1.9%
s 92
 
1.9%
u 81
 
1.6%
o 81
 
1.6%
p 81
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4914
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 985
20.0%
r 985
20.0%
E 893
18.2%
t 812
16.5%
h 812
16.5%
M 92
 
1.9%
s 92
 
1.9%
u 81
 
1.6%
o 81
 
1.6%
p 81
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4914
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 985
20.0%
r 985
20.0%
E 893
18.2%
t 812
16.5%
h 812
16.5%
M 92
 
1.9%
s 92
 
1.9%
u 81
 
1.6%
o 81
 
1.6%
p 81
 
1.6%

Transported
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
0
985 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters985
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 985
100.0%

Length

2024-05-07T12:36:09.493874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T12:36:09.644811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 985
100.0%

Most occurring characters

ValueCountFrequency (%)
0 985
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 985
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 985
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 985
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 985
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 985
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 985
100.0%

Consumption_High_End
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct178
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.639534
Minimum0
Maximum3213
Zeros710
Zeros (%)72.1%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2024-05-07T12:36:09.791063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile465
Maximum3213
Range3213
Interquartile range (IQR)5

Descriptive statistics

Standard deviation348.1085
Coefficient of variation (CV)3.8405814
Kurtosis32.613976
Mean90.639534
Median Absolute Deviation (MAD)0
Skewness5.4116005
Sum89279.941
Variance121179.53
MonotonicityNot monotonic
2024-05-07T12:36:10.042290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 710
72.1%
7 9
 
0.9%
4 9
 
0.9%
1 6
 
0.6%
2 6
 
0.6%
3 6
 
0.6%
5 5
 
0.5%
21 4
 
0.4%
55 4
 
0.4%
9 4
 
0.4%
Other values (168) 222
 
22.5%
ValueCountFrequency (%)
0 710
72.1%
1 6
 
0.6%
2 6
 
0.6%
3 6
 
0.6%
4 9
 
0.9%
5 5
 
0.5%
7 9
 
0.9%
7.395469138 1
 
0.1%
8 2
 
0.2%
9 4
 
0.4%
ValueCountFrequency (%)
3213 1
0.1%
2956 1
0.1%
2800 1
0.1%
2787 1
0.1%
2729 1
0.1%
2619 1
0.1%
2236.295233 1
0.1%
2099 1
0.1%
1941 1
0.1%
1921 1
0.1%

Consumption_Basic
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct281
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean469.63694
Minimum0
Maximum9532
Zeros660
Zeros (%)67.0%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2024-05-07T12:36:10.288469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3722
95-th percentile2055.6
Maximum9532
Range9532
Interquartile range (IQR)722

Descriptive statistics

Standard deviation1032.6203
Coefficient of variation (CV)2.198763
Kurtosis21.763123
Mean469.63694
Median Absolute Deviation (MAD)0
Skewness4.0657802
Sum462592.38
Variance1066304.8
MonotonicityNot monotonic
2024-05-07T12:36:10.504616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 660
67.0%
702 5
 
0.5%
863 3
 
0.3%
1721 3
 
0.3%
879 3
 
0.3%
717 3
 
0.3%
736 3
 
0.3%
991 2
 
0.2%
653 2
 
0.2%
838 2
 
0.2%
Other values (271) 299
30.4%
ValueCountFrequency (%)
0 660
67.0%
9 1
 
0.1%
13.7364642 1
 
0.1%
404 1
 
0.1%
441 2
 
0.2%
443 1
 
0.1%
469 1
 
0.1%
524 1
 
0.1%
534 1
 
0.1%
537 1
 
0.1%
ValueCountFrequency (%)
9532 1
0.1%
8525 1
0.1%
8252 1
0.1%
7201 1
0.1%
6819 1
0.1%
6455.817265 1
0.1%
6073 1
0.1%
6050 1
0.1%
5682 1
0.1%
5407 1
0.1%

Age_group
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
Young adults
535 
Minor
278 
Middle-aged
154 
Senior
 
18

Length

Max length12
Median length12
Mean length9.7583756
Min length5

Characters and Unicode

Total characters9612
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYoung adults
2nd rowYoung adults
3rd rowMiddle-aged
4th rowYoung adults
5th rowMinor

Common Values

ValueCountFrequency (%)
Young adults 535
54.3%
Minor 278
28.2%
Middle-aged 154
 
15.6%
Senior 18
 
1.8%

Length

2024-05-07T12:36:10.769207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T12:36:10.949283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
young 535
35.2%
adults 535
35.2%
minor 278
18.3%
middle-aged 154
 
10.1%
senior 18
 
1.2%

Most occurring characters

ValueCountFrequency (%)
u 1070
11.1%
d 997
10.4%
n 831
 
8.6%
o 831
 
8.6%
l 689
 
7.2%
g 689
 
7.2%
a 689
 
7.2%
t 535
 
5.6%
s 535
 
5.6%
Y 535
 
5.6%
Other values (7) 2211
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9612
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 1070
11.1%
d 997
10.4%
n 831
 
8.6%
o 831
 
8.6%
l 689
 
7.2%
g 689
 
7.2%
a 689
 
7.2%
t 535
 
5.6%
s 535
 
5.6%
Y 535
 
5.6%
Other values (7) 2211
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9612
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 1070
11.1%
d 997
10.4%
n 831
 
8.6%
o 831
 
8.6%
l 689
 
7.2%
g 689
 
7.2%
a 689
 
7.2%
t 535
 
5.6%
s 535
 
5.6%
Y 535
 
5.6%
Other values (7) 2211
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9612
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 1070
11.1%
d 997
10.4%
n 831
 
8.6%
o 831
 
8.6%
l 689
 
7.2%
g 689
 
7.2%
a 689
 
7.2%
t 535
 
5.6%
s 535
 
5.6%
Y 535
 
5.6%
Other values (7) 2211
23.0%

Interactions

2024-05-07T12:36:02.684348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:52.045265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:53.481819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:54.973990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:56.538331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:58.056945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:59.472505image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:36:01.030210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:36:02.886368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:52.211400image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:53.631245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:55.161136image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:56.699865image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:58.231387image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:59.681175image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:36:01.293905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:36:03.072939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:52.379111image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:53.819228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:55.361821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:56.903381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:58.401724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:59.873039image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:36:01.497169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:36:03.264004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:52.546693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:54.021806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:55.534806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:57.065876image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:58.563000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:36:00.128950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:36:01.655603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:36:03.444254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:52.721761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:54.222381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:55.711354image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:57.240450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:58.754736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:36:00.320097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:36:01.828238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:36:03.621301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:52.900883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:54.414064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:55.892945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:57.425581image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:58.942954image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:36:00.508545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:36:02.098553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:36:03.804903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:53.119551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:54.597048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:56.110228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:57.650560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:59.113056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:36:00.678784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:36:02.287020image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:36:03.962216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:53.319884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:54.778592image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:56.311602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:57.851589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:35:59.301291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:36:00.851482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T12:36:02.472157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-05-07T12:36:11.111906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Age_groupCabin_deckConsumption_BasicConsumption_High_EndCryoSleepDestinationFoodCourtGroup_sizeHomePlanetRoomServiceShoppingMallSpaVIPVRDeck
Age_group1.0000.1480.1170.1160.0000.0820.086-0.2240.1520.0770.1140.0530.0820.078
Cabin_deck0.1481.000-0.617-0.5820.5800.111-0.5190.0170.804-0.389-0.430-0.4540.354-0.465
Consumption_Basic0.117-0.6171.0000.8960.4290.1550.807-0.0990.4740.6070.7560.6780.2950.654
Consumption_High_End0.116-0.5820.8961.0000.2760.1370.736-0.0840.4850.6950.6690.7510.3620.730
CryoSleep0.0000.5800.4290.2761.0000.200-0.604-0.0570.246-0.466-0.600-0.4820.102-0.472
Destination0.0820.1110.1550.1370.2001.000-0.0300.0430.128-0.010-0.015-0.0280.050-0.049
FoodCourt0.086-0.5190.8070.736-0.604-0.0301.000-0.0950.4390.4560.4000.5940.2930.582
Group_size-0.2240.017-0.099-0.084-0.0570.043-0.0951.0000.118-0.073-0.114-0.0160.000-0.044
HomePlanet0.1520.8040.4740.4850.2460.1280.4390.1181.0000.2640.1440.2850.2420.249
RoomService0.077-0.3890.6070.695-0.466-0.0100.456-0.0730.2641.0000.5050.3440.0000.343
ShoppingMall0.114-0.4300.7560.669-0.600-0.0150.400-0.1140.1440.5051.0000.4540.2970.410
Spa0.053-0.4540.6780.751-0.482-0.0280.594-0.0160.2850.3440.4541.0000.3040.518
VIP0.0820.3540.2950.3620.1020.0500.2930.0000.2420.0000.2970.3041.0000.154
VRDeck0.078-0.4650.6540.730-0.472-0.0490.582-0.0440.2490.3430.4100.5180.1541.000

Missing values

2024-05-07T12:36:04.223750image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-07T12:36:04.627549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CryoSleepDestinationVIPRoomServiceFoodCourtShoppingMallSpaVRDeckCabin_deckGroup_sizeHomePlanetTransportedConsumption_High_EndConsumption_BasicAge_group
0FalseTRAPPIST-1eFalse0.00.00.00.00.0B1Europa00.00.0Young adults
15FalseTRAPPIST-1eFalse32.00.0876.00.00.0G1Earth032.0876.0Young adults
22True55 Cancri eFalse0.00.00.00.00.0E6Earth00.00.0Middle-aged
23True55 Cancri eFalse0.00.00.00.00.0E6Earth00.00.0Young adults
25TruePSO J318.5-22False0.00.00.00.00.0E6Earth00.00.0Minor
26FalseTRAPPIST-1eFalse0.00.00.00.00.0E6Earth00.00.0Minor
30False55 Cancri eFalse22.06073.00.01438.0328.0C1Europa01788.06073.0Young adults
40True55 Cancri eFalse0.00.00.00.00.0G1Earth00.00.0Young adults
43TrueTRAPPIST-1eFalse0.00.00.00.00.0G3Earth00.00.0Middle-aged
55TruePSO J318.5-22False0.00.00.00.00.0G1Earth00.00.0Young adults
CryoSleepDestinationVIPRoomServiceFoodCourtShoppingMallSpaVRDeckCabin_deckGroup_sizeHomePlanetTransportedConsumption_High_EndConsumption_BasicAge_group
8613TrueTRAPPIST-1eFalse0.00.00.00.00.0G5Earth00.00.0Minor
8617FalseTRAPPIST-1eFalse0.00.00.00.00.0G5Earth00.00.0Minor
8625TrueTRAPPIST-1eFalse0.00.00.00.00.0G1Earth00.00.0Young adults
8631TrueTRAPPIST-1eFalse0.00.00.00.00.0G1Earth00.00.0Young adults
8638TruePSO J318.5-22False0.00.00.00.00.0G1Earth00.00.0Senior
8640FalseTRAPPIST-1eFalse39.00.01085.024.00.0F1Mars063.01085.0Minor
8642TruePSO J318.5-22False0.00.00.00.00.0G1Earth00.00.0Young adults
8652FalseTRAPPIST-1eFalse1.01146.00.050.034.0A3Europa085.01146.0Young adults
8654False55 Cancri eTrue0.06819.00.01643.074.0A1Europa01717.06819.0Middle-aged
8655TruePSO J318.5-22False0.00.00.00.00.0G1Earth00.00.0Young adults

Duplicate rows

Most frequently occurring

CryoSleepDestinationVIPRoomServiceFoodCourtShoppingMallSpaVRDeckCabin_deckGroup_sizeHomePlanetTransportedConsumption_High_EndConsumption_BasicAge_group# duplicates
49TrueTRAPPIST-1eFalse0.00.00.00.00.0G1Earth00.00.0Young adults122
29TruePSO J318.5-22False0.00.00.00.00.0G1Earth00.00.0Young adults58
47TrueTRAPPIST-1eFalse0.00.00.00.00.0G1Earth00.00.0Minor34
14FalseTRAPPIST-1eFalse0.00.00.00.00.0G3Earth00.00.0Minor29
46TrueTRAPPIST-1eFalse0.00.00.00.00.0G1Earth00.00.0Middle-aged27
54TrueTRAPPIST-1eFalse0.00.00.00.00.0G3Earth00.00.0Minor20
41TrueTRAPPIST-1eFalse0.00.00.00.00.0E1Mars00.00.0Young adults16
55TrueTRAPPIST-1eFalse0.00.00.00.00.0G3Earth00.00.0Young adults16
52TrueTRAPPIST-1eFalse0.00.00.00.00.0G2Earth00.00.0Young adults15
27TruePSO J318.5-22False0.00.00.00.00.0G1Earth00.00.0Middle-aged14